Language deficits have devastating effects on one's ability to function in society. Designing appropriate interventions depends in part on understanding spoken language processing in healthy adults. Indeed, similarity metrics based on spoken word recognition research have allowed the design of more sensitive tests for hearing and language deficits. In this proposal, four projects examine the effects on spoken word recognition of the temporal distribution of similarity in spoken words, learning, and top-down knowledge. Time course measures are obtained from eye tracking during visually guided tasks under spoken instructions. The eye tracking is complemented by more traditional paradigms, allowing direct comparisons of the measures and providing data for items not amenable to eye tracking. Natural English stimuli and artificial lexicons are used as stimuli. Real words do not fall into conveniently balanced levels on the dimensions of interest, while artificial lexicons allow precise control over phonological similarity and frequency, and therefore competition neighborhood. They also provide a paradigm for studying learning, whether in the case of new words or changes in the relative frequencies of competitors. The results of the projects are used to refine similarity metrics for spoken words and develop a computational model of spoken word processing and learning.